A Hidden Markov Model-Based Approach with an Adaptive Threshold Model for Off-Line Arabic Handwriting Recognition

In contrast to the mainstream HMM-based approaches dedicated for the recognition of offline handwritten Arabic, this paper proposes an HMM-based approach that built upon an explicit segmentation module. And shape representative based rather than sliding window based features, are extracted and used to build a reference as well as a confirmation model for each letter in each handwritten form. Additionally, we constructed an HMM-based threshold model by ergodically connecting all letter models, in order to detect false segmentation as well as nonletter segments. IESK-arDB and IFN/ENIT databases are used for testing and evaluation of the proposed approach respectively, and satisfactory results are achieved.